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2月6日周五
2月5日周四
2月4日周三
  7. 埃森工业空天新闻历史资料

    十部门发文推动低空经济标准体系建设，中科星图五大领域创新护航产业行稳致远

    作为低空经济产业的推动者，中科星图紧扣国家战略导向，早已布局低空经济领域，凭借在空天信息领域的深厚积累，构建了全链条产品体系与服务能力，高度契合此次标准体系建设内容，并将在“指南”的指引下，进一步优化产品技术，赋能千行百业。（披露日：2026-02-04）

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2月3日周二
  1. arXiv eVTOL预印本历史资料

    Impact of Physics-Informed Features on Neural Network Complexity for Li-ion Battery Voltage Prediction in Electric Vertical Takeoff and Landing Aircrafts

    The electrification of vertical takeoff and landing aircraft demands high-fidelity battery management systems capable of predicting voltage response under aggressive power dynamics. While data-driven models offer high accuracy, they often require complex architectures and extensive training data. Conversely, equivalent circuit models (ECMs), such as the second-order model, offer physical interpretability but struggle with high C-rate non-linearities. This paper investigates the impact of integrating physics-based information into data-driven surrogate models. Specifically, we evaluate whether physics-informed features allow for the simplification of neural network architectures without compromising accuracy. Using the open-source electric vertical takeoff and landing (eVTOL) battery dataset, we compare pure data-driven models against physics-informed data models. Results demonstrate that physics-informed models achieve comparable accuracy to complex pure data-driven models while using up to 75% fewer trainable parameters, significantly reducing computational overhead for potential on-board deployment.（预印本；同行评审状态请核对原文。）

2月2日周一
1月31日周六
1月30日周五
1月29日周四
  1. arXiv先进空中交通研究历史资料

    Heterogeneous Vertiport Selection Optimization for On-Demand Air Taxi Services: A Deep Reinforcement Learning Approach

    Urban Air Mobility (UAM) has emerged as a transformative solution to alleviate urban congestion by utilizing low-altitude airspace, thereby reducing pressure on ground transportation networks. To enable truly efficient and seamless door-to-door travel experiences, UAM requires close integration with existing ground transportation infrastructure. However, current research on optimal integrated routing strategies for passengers in air-ground mobility systems remains limited, with a lack of systematic exploration.To address this gap, we first propose a unified optimization model that integrates strategy selection for both air and ground transportation. This model captures the dynamic characteristics of multimodal transport networks and incorporates real-time traffic conditions alongside passenger decision-making behavior. Building on this model, we propose a Unified Air-Ground Mobility Coordination (UAGMC) framework, which leverages deep reinforcement learning (RL) and Vehicle-to-Everything (V2X) communication to optimize vertiport selection and dynamically plan air taxi routes. Experimental results demonstrate that UAGMC achieves a 34\% reduction in average travel time compared to conventional proportional allocation methods, enhancing overall travel efficiency and providing novel insights into the integration and optimization of multimodal transportation systems. This work lays a solid foundation for advancing intelligent urban mobility solutions through the coordination of air and ground transportation modes. The related code can be found at https://github.com/Traffic-Alpha（预印本；同行评审状态请核对原文。）